Last updated: 2024-11-24
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Knit directory: demor2/
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Some notes on data tidying from the tidyr site.
pivot_longer()
names_prefix= argument: remove matching text form the
start of each variable name.names_sep= argument: specify positions or regular
expression to break on.names_pattern= argument: specify regular expression to
split variable name into multiple columns.names_transform= argument: convert variable type to
integer.values_drop_na= argument: drop NA rows if
TRUE.Regular Expressions
.: matches any character.?: 0 or 1.*: 0 or more.# Numeric data in columns names
billboard %>%
pivot_longer(
cols = tidyselect::starts_with("wk"),
names_to = "week",
names_prefix = "wk",
names_transform = as.integer, # equivalent: readr::parse_number
values_to = "rank",
values_drop_na = TRUE,
)# A tibble: 5,307 × 5
artist track date.entered week rank
<chr> <chr> <date> <int> <dbl>
1 2 Pac Baby Don't Cry (Keep... 2000-02-26 1 87
2 2 Pac Baby Don't Cry (Keep... 2000-02-26 2 82
3 2 Pac Baby Don't Cry (Keep... 2000-02-26 3 72
4 2 Pac Baby Don't Cry (Keep... 2000-02-26 4 77
5 2 Pac Baby Don't Cry (Keep... 2000-02-26 5 87
6 2 Pac Baby Don't Cry (Keep... 2000-02-26 6 94
7 2 Pac Baby Don't Cry (Keep... 2000-02-26 7 99
8 2Ge+her The Hardest Part Of ... 2000-09-02 1 91
9 2Ge+her The Hardest Part Of ... 2000-09-02 2 87
10 2Ge+her The Hardest Part Of ... 2000-09-02 3 92
# ℹ 5,297 more rows# Many variables in column names
(who_wide = who %>%
pivot_longer(
cols = new_sp_m014:newrel_f65,
names_to = c("diagnosis", "gender", "age"),
names_pattern = "new_?(.*)_(.)(.*)",
names_transform = list(
gender = as.factor, # equivalent: readr::parse_factor
age = ~ readr::parse_factor(
.x,
levels = c("014", "1524", "2534", "3544", "4554", "5564", "65"),
ordered = TRUE
)
),
values_to = "count",
values_drop_na = TRUE,
))# A tibble: 76,046 × 8
country iso2 iso3 year diagnosis gender age count
<chr> <chr> <chr> <dbl> <chr> <fct> <ord> <dbl>
1 Afghanistan AF AFG 1997 sp m 014 0
2 Afghanistan AF AFG 1997 sp m 1524 10
3 Afghanistan AF AFG 1997 sp m 2534 6
4 Afghanistan AF AFG 1997 sp m 3544 3
5 Afghanistan AF AFG 1997 sp m 4554 5
6 Afghanistan AF AFG 1997 sp m 5564 2
7 Afghanistan AF AFG 1997 sp m 65 0
8 Afghanistan AF AFG 1997 sp f 014 5
9 Afghanistan AF AFG 1997 sp f 1524 38
10 Afghanistan AF AFG 1997 sp f 2534 36
# ℹ 76,036 more rows# Multiple observations per row
household %>%
pivot_longer(
cols = !family,
names_to = c(".value", "child"), # part of the column name specify the value being measured
names_sep = "_",
values_drop_na = TRUE
)# A tibble: 9 × 4
family child dob name
<int> <chr> <date> <chr>
1 1 child1 1998-11-26 Susan
2 1 child2 2000-01-29 Jose
3 2 child1 1996-06-22 Mark
4 3 child1 2002-07-11 Sam
5 3 child2 2004-04-05 Seth
6 4 child1 2004-10-10 Craig
7 4 child2 2009-08-27 Khai
8 5 child1 2000-12-05 Parker
9 5 child2 2005-02-28 Graciepivot_wider()
name_glue= argument: use name_from columns
to create custom column names.name_expand= argument: show implicit factor levels if
TRUE.values_fill= argument: specify the value filled in with
when missing.values_fn= argument: specify the function apply to the
value.unused_fn= argument: summarize the values from the
unused column by specified function.# Capture-recapture data
fish_encounters %>%
pivot_wider(
names_from = station,
values_from = seen,
values_fill = 0
)# A tibble: 19 × 12
fish Release I80_1 Lisbon Rstr Base_TD BCE BCW BCE2 BCW2 MAE MAW
<fct> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 4842 1 1 1 1 1 1 1 1 1 1 1
2 4843 1 1 1 1 1 1 1 1 1 1 1
3 4844 1 1 1 1 1 1 1 1 1 1 1
4 4845 1 1 1 1 1 0 0 0 0 0 0
5 4847 1 1 1 0 0 0 0 0 0 0 0
6 4848 1 1 1 1 0 0 0 0 0 0 0
7 4849 1 1 0 0 0 0 0 0 0 0 0
8 4850 1 1 0 1 1 1 1 0 0 0 0
9 4851 1 1 0 0 0 0 0 0 0 0 0
10 4854 1 1 0 0 0 0 0 0 0 0 0
11 4855 1 1 1 1 1 0 0 0 0 0 0
12 4857 1 1 1 1 1 1 1 1 1 0 0
13 4858 1 1 1 1 1 1 1 1 1 1 1
14 4859 1 1 1 1 1 0 0 0 0 0 0
15 4861 1 1 1 1 1 1 1 1 1 1 1
16 4862 1 1 1 1 1 1 1 1 1 0 0
17 4863 1 1 0 0 0 0 0 0 0 0 0
18 4864 1 1 0 0 0 0 0 0 0 0 0
19 4865 1 1 1 0 0 0 0 0 0 0 0# Aggregation
warpbreaks %>%
pivot_wider(
names_from = wool,
values_from = breaks,
values_fn = mean
)# A tibble: 3 × 3
tension A B
<fct> <dbl> <dbl>
1 L 44.6 28.2
2 M 24 28.8
3 H 24.6 18.8# Generate column name from multiple variables
who_wide %>%
pivot_wider(
names_from = c(diagnosis, gender, age),
values_from = count,
names_glue = "cnt_{diagnosis}_{gender}_{age}"
)# A tibble: 3,484 × 60
country iso2 iso3 year cnt_sp_m_014 cnt_sp_m_1524 cnt_sp_m_2534
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan AF AFG 1997 0 10 6
2 Afghanistan AF AFG 1998 30 129 128
3 Afghanistan AF AFG 1999 8 55 55
4 Afghanistan AF AFG 2000 52 228 183
5 Afghanistan AF AFG 2001 129 379 349
6 Afghanistan AF AFG 2002 90 476 481
7 Afghanistan AF AFG 2003 127 511 436
8 Afghanistan AF AFG 2004 139 537 568
9 Afghanistan AF AFG 2005 151 606 560
10 Afghanistan AF AFG 2006 193 837 791
# ℹ 3,474 more rows
# ℹ 53 more variables: cnt_sp_m_3544 <dbl>, cnt_sp_m_4554 <dbl>,
# cnt_sp_m_5564 <dbl>, cnt_sp_m_65 <dbl>, cnt_sp_f_014 <dbl>,
# cnt_sp_f_1524 <dbl>, cnt_sp_f_2534 <dbl>, cnt_sp_f_3544 <dbl>,
# cnt_sp_f_4554 <dbl>, cnt_sp_f_5564 <dbl>, cnt_sp_f_65 <dbl>,
# cnt_sn_m_014 <dbl>, cnt_sn_m_1524 <dbl>, cnt_sn_m_2534 <dbl>,
# cnt_sn_m_3544 <dbl>, cnt_sn_m_4554 <dbl>, cnt_sn_m_5564 <dbl>, …# Tidy census
daily %>%
pivot_wider(
names_from = day,
values_from = value,
names_expand = TRUE,
values_fill = 0
)# A tibble: 4 × 5
...1 Fri Mon Thu Tue
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 0 0 0 2
2 2 0 0 3 0
3 3 1 0 0 0
4 4 0 5 0 0# Unused columns
updates %>%
pivot_wider(
id_cols = county,
names_from = system,
values_from = value,
unused_fn = list(date = max)
)# A tibble: 2 × 5
county A B C date
<chr> <dbl> <dbl> <dbl> <date>
1 Wake 3.2 4 5.5 2020-01-03
2 Guilford 2 NA 1.2 2020-01-04
sessionInfo()R version 4.4.0 (2024-04-24)
Platform: aarch64-apple-darwin20
Running under: macOS 15.1.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/Detroit
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
[5] purrr_1.0.2 readr_2.1.5 tibble_3.2.1 ggplot2_3.5.1
[9] tidyverse_2.0.0 tidyr_1.3.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] sass_0.4.9 utf8_1.2.4 generics_0.1.3 stringi_1.8.4
[5] hms_1.1.3 digest_0.6.37 magrittr_2.0.3 timechange_0.3.0
[9] evaluate_1.0.0 grid_4.4.0 fastmap_1.2.0 rprojroot_2.0.4
[13] jsonlite_1.8.9 processx_3.8.4 whisker_0.4.1 ps_1.7.6
[17] promises_1.3.0 httr_1.4.7 fansi_1.0.6 scales_1.3.0
[21] jquerylib_0.1.4 cli_3.6.3 crayon_1.5.2 rlang_1.1.4
[25] bit64_4.0.5 munsell_0.5.1 withr_3.0.1 cachem_1.1.0
[29] yaml_2.3.10 parallel_4.4.0 tools_4.4.0 tzdb_0.4.0
[33] colorspace_2.1-1 httpuv_1.6.15 vctrs_0.6.5 R6_2.5.1
[37] lifecycle_1.0.4 git2r_0.33.0 bit_4.0.5 fs_1.6.4
[41] vroom_1.6.5 pkgconfig_2.0.3 callr_3.7.6 pillar_1.9.0
[45] bslib_0.8.0 later_1.3.2 gtable_0.3.5 glue_1.7.0
[49] Rcpp_1.0.13 xfun_0.47 tidyselect_1.2.1 rstudioapi_0.16.0
[53] knitr_1.48 htmltools_0.5.8.1 rmarkdown_2.28 compiler_4.4.0
[57] getPass_0.2-4